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mitigating jamming and spoofing threats in real-time. Integration of Trusted Execution Environments (TEEs): Investigate the use of TEEs to create secure zones within embedded systems, facilitating secure data
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quantification and data science. Potential investigation areas: • Enhancing Monte Carlo and Markov Chain Monte Carlo (MCMC) with reinforcement learning. • Developing adaptive tuning and continual learning
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resilient, long-term cost-benefit case for modernising and protecting our grid. The project will complement the ISS initiative and model data-driven grid optimisation services that ISSs can enable. Firstly
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of the GAG biosynthetic machinery Applying novel GAG analytical technologies to investigate how changes in GAG structure and organisation drive and/or respond to shifting developmental stages. Working
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-Based Modelling ✔ Data Science & Advanced Analytics with real-world SCADA data ✔ Renewable Energy Systems & Wind Turbine Engineering ✔ Industry Engagement & Research Commercialization ✔ Scientific Writing
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Requirements We will only consider candidates with a good Bachelor’s degree (ideally, a first, minimum 2:1) and/or a master’s degree in computer science, data analytics, health economics, or computational
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application, polygraph data capture and analysis has received limited systematic research and does not yet incorporate modern sensors, computing and analytical techniques. Project: This project aims to explore
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and work independently, excellent analytical skills and strong verbal and written communication skills are also essential requirements. How to apply You must apply through the University’s Apply
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state-of-the-art analytical facilities. You’ll also be trained in advanced techniques such as drone-based macrophyte surveys and remote autosamplers for phosphorus monitoring. This PhD combines ecology
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place value on prior experience, enthusiasm for research, and the ability to think and work independently. Excellent Analytical skills and strong verbal and written communication skills are also essential